Multi-modal image sharpening in fourier transform infrared (FTIR) microscopy. Issue 15 (1st July 2021)

• Record Type: Journal Article
• Authors: Mankar, Rupali; Gajjela, Chalapathi Charan; Shahraki, Farideh Foroozandeh; Prasad, Saurabh; Mayerich, David; Reddy, Rohith
• Is Part Of: Analyst. Volume 146:Issue 15(2021)
• Page Start: 4822
• Page End: 4834
• Publication Date: 2021-07-01
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/d1an00103e
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Multi-modal fusion improves spatial resolution of FTIR images beyond diffraction-limit that improves classification of histology classes. Enhanced spatial details are comparable to O-PTIR which is a super-resolution spectroscopic imaging technology. Abstract : Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 μm to 12.5 μm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 μm to 12 μm), limiting the maximum resolving power to between 3 μm and 6 μm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy.